Planning Where states are transparent and actions have preconditions and effects Notes at

Top 10 reasons why these lectures are probably going to be a bust 10. My energy levels go down as Sun does 9. In 14 years at ASU, this is the first time I am doing two lectures on the same day (afternoon!) 8. I haven’t had any intravenous espresso 7. I have little mental picture of where you guys are coming from… 6…..(Skip 6 to 2) 1. Huan Liu is the winner of dept best teacher award (and I am not…)..and (now that your expectations have driven down to subterranean levels) ONE killer reason you should pay attention nevertheless…

A: A Unified Brand-name-Free Introduction to Planning Subbarao Kambhampati The whole point of AI is Planning & Acting Environment action perception Goals (Static vs. Dynamic) (Observable vs. Partially Observable) (perfect vs. Imperfect) (Deterministic vs. Stochastic) What action next? (Instantaneous vs. Durative) (Full vs. Partial satisfaction) The $$$$$$ Question

A: A Unified Brand-name-Free Introduction to Planning Subbarao Kambhampati Static Deterministic ObservableInstantaneousPropositional “Classical Planning” Dynamic Replanning / Situated Plans Durative Temporal Reasoning Continuous Numeric Constraint reasoning (LP/ILP) Stochastic MDP Policies POMDP Policies Partially Observable Contingent/Conforma nt Plans, Interleaved execution Semi-MDP Policies

Deterministic Planning Given an initial state I, a goal state G and a set of actions A:{a1…an} Find a sequence of actions that when applied from the initial state will lead the agent to the goal state. Qn: Why is this not just a search problem (with actions being operators?) –Answer: We have “factored” representations of states and actions. And we can use this internal structure to our advantage in –Formulating the search (forward/backward/insideout) –deriving more powerful heuristics etc.

State Variable Models World is made up of states which are defined in terms of state variables –Can be boolean (or multi-ary or continuous) States are complete assignments over state variables –So, k boolean state variables can represent how many states? Actions change the values of the state variables –Applicability conditions of actions are also specified in terms of partial assignments over state variables

CSE 574: Planning & Learning Subbarao Kambhampati Transition Sytems Perspective G We can think of the agent-environment dynamics in terms of the transition systems –A transition system is a 2-tuple where »S is a set of states »A is a set of actions, with each action a being a subset of SXS –Transition systems can be seen as graphs with states corresponding to nodes, and actions corresponding to edges »If transitions are not deterministic, then the edges will be “hyper- edges”—i.e. will connect sets of states to sets of states –The agent may know that its initial state is some subset S’ of S »If the environment is not fully observable, then |S’|>1. –It may consider some subset Sg of S as desirable states –Finding a plan is equivalent to finding (shortest) paths in the graph corresponding to the transition system »Search graph is the same as transition graph for deterministic planning »For non-deterministic actions and/or partially observable environments, the search is in the space of sets of states (called belief states 2 S ) These were discussed orally but were not shown in the class

CSE 574: Planning & Learning Subbarao Kambhampati Transition System Models A transition system is a two tuple Where S is a set of “states” A is a set of “transitions” each transition a is a subset of SXS --If a is a (partial) function then deterministic transition --otherwise, it is a “non-deterministic” transition --It is a stochastic transition If there are probabilities associated with each state a takes s to --Finding plans becomes is equivalent to finding “paths” in the transition system Transition system models are called “Explicit state-space” models In general, we would like to represent the transition systems more compactly e.g. State variable representation of states. These latter are called “Factored” models Each action in this model can be Represented by incidence matrices (e.g. below) The set of all possible transitions Will then simply be the SUM of the Individual incidence matrices Transitions entailed by a sequence of actions will be given by the (matrix) multiplication of the incidence matrices These were discussed orally but were not shown in the class

CSE 574: Planning & Learning Subbarao Kambhampati Problems with transition systems G Transition systems are a great conceptual tool to understand the differences between the various planning problems G …However direct manipulation of transition systems tends to be too cumbersome –The size of the explicit graph corresponding to a transition system is often very large (see Homework 1 problem 1) –The remedy is to provide “compact” representations for transition systems »Start by explicating the structure of the “states” l e.g. states specified in terms of state variables »Represent actions not as incidence matrices but rather functions specified directly in terms of the state variables l An action will work in any state where some state variables have certain values. When it works, it will change the values of certain (other) state variables These were discussed orally but were not shown in the class

CSE 574: Planning & Learning Subbarao Kambhampati Why is this more compact? (than explicit transition systems) G In explicit transition systems actions are represented as state-to-state transitions where in each action will be represented by an incidence matrix of size |S|x|S| G In state-variable model, actions are represented only in terms of state variables whose values they care about, and whose value they affect. G Consider a state space of 1024 states. It can be represented by log =10 state variables. If an action needs variable v1 to be true and makes v7 to be false, it can be represented by just 2 bits (instead of a 1024x1024 matrix) –Of course, if the action has a complicated mapping from states to states, in the worst case the action rep will be just as large –The assumption being made here is that the actions will have effects on a small number of state variables. These were discussed orally but were not shown in the class

Blocks world State variables: Ontable(x) On(x,y) Clear(x) hand-empty holding(x) Stack(x,y) Prec: holding(x), clear(y) eff: on(x,y), ~cl(y), ~holding(x), hand-empty Unstack(x,y) Prec: on(x,y),hand-empty,cl(x) eff: holding(x),~clear(x),clear(y),~hand-empty Pickup(x) Prec: hand-empty,clear(x),ontable(x) eff: holding(x),~ontable(x),~hand-empty,~Clear(x) Putdown(x) Prec: holding(x) eff: Ontable(x), hand-empty,clear(x),~holding(x) Initial state: Complete specification of T/F values to state variables --By convention, variables with F values are omitted Goal state: A partial specification of the desired state variable/value combinations --desired values can be both positive and negative Init: Ontable(A),Ontable(B), Clear(A), Clear(B), hand-empty Goal: ~clear(B), hand-empty All the actions here have only positive preconditions; but this is not necessary

Progression: An action A can be applied to state S iff the preconditions are satisfied in the current state The resulting state S’ is computed as follows: --every variable that occurs in the actions effects gets the value that the action said it should have --every other variable gets the value it had in the state S where the action is applied Ontable(A) Ontable(B), Clear(A) Clear(B) hand-empty holding(A) ~Clear(A) ~Ontable(A) Ontable(B), Clear(B) ~handempty Pickup(A) Pickup(B) holding(B) ~Clear(B) ~Ontable(B) Ontable(A), Clear(A) ~handempty

Generic (progression) planner Goal test(S,G)—check if every state variable in S, that is mentioned in G, has the value that G gives it. Child generator(S,A) –For each action a in A do If every variable mentioned in Prec(a) has the same value in it and S –Then return Progress(S,a) as one of the children of S »Progress(S,A) is a state S’ where each state variable v has value v[Eff(a)]if it is mentioned in Eff(a) and has the value v[S] otherwise Search starts from the initial state

Domain model for Have-Cake and Eat-Cake problem

Regression: A state S can be regressed over an action A (or A is applied in the backward direction to S) Iff: --There is no variable v such that v is given different values by the effects of A and the state S --There is at least one variable v’ such that v’ is given the same value by the effects of A as well as state S The resulting state S’ is computed as follows: -- every variable that occurs in S, and does not occur in the effects of A will be copied over to S’ with its value as in S -- every variable that occurs in the precondition list of A will be copied over to S’ with the value it has in in the precondition list ~clear(B) hand-empty Putdown(A) Stack(A,B) ~clear(B) holding(A) clear(B) Putdown(B)?? Termination test: Stop when the state s’ is entailed by the initial state s I *Same entailment dir as before..

Progression vs. Regression The never ending war.. Part 1 Progression has higher branching factor Progression searches in the space of complete (and consistent) states Regression has lower branching factor Regression searches in the space of sets of states There are 2 s sets and umpteen syntactically different empty states  You can also do bidirectional search stop when a (leaf) state in the progression tree entails a (leaf) state (formula) in the regression tree